Mortality Risk Evaluation: A Proposal for Intensive Care Units Patients Exploring Machine Learning Methods-Reference-Cited by-同舟云学术

Mortality Risk Evaluation: A Proposal for Intensive Care Units Patients Exploring Machine Learning Methods

Published:2022 Issue: Volume: Page:1-14
ISSN:0302-9743
Container-title:Intelligent Systems
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Author:

de Souza Alexandre Renato Rodrigues^ORCID,Ferreira Fabrício Neitzke^ORCID,Lambrecht Rodrigo Blanke^ORCID,Reichow Leonardo Costa^ORCID,Santos Helida Salles^ORCID,Reiser Renata Hax Sander^ORCID,Yamin Adenauer Correa^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-21686-2_1

Reference21 articles.

1. Alghatani, K., Ammar, N., Rezgui, A., Shaban-Nejad, A.: Predicting intensive care unit length of stay and mortality using patient vital signs: machine learning model development and validation (2021). https://doi.org/10.2196/21347

2. Awad, A., Bader-El-Den, M., McNicholas, J., Briggs, J., El-Sonbaty, Y.: Predicting hospital mortality for intensive care unit patients: time-series analysis. Health Inf. J. 26(2), 1043–1059 (2020). https://doi.org/10.1177/1460458219850323, http://journals.sagepub.com/doi/10.1177/1460458219850323

3. Baker, S., Xiang, W., Atkinson, I.: Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach (2020). https://doi.org/10.1038/s41598-020-78184-7

4. Barella, V.H., Garcia, L.P.F., de Carvalho, A.C.P.L.F.: Simulating complexity measures on imbalanced datasets. In: 9th Brazilian Conference on Intelligent Systems (BRACIS) (2020)

5. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)

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